4 Software Management

Learning Objectives

Use the module tool to search for and load pre-installed software.
Describe what a package manager is, and how it can be used to manage software instalation on a HPC environment.
Install the Mamba package manager.
Create a software environment and install software using Mamba.
Describe what a software container is and how it differs from a package manager.
Download and use a pre-existing software container for bioinformatics and scientific computing applications.

4.1 Using pre-installed software

It is very often the case that HPC admins have pre-installed several software packages that are regularly used by their users. Because there can be a large number of packages (and often different versions of the same program), you need to load the programs you want to use in your script using the module tool.

The following table summarises the most common commands for this tool:

Command	Description
`module avail`	List all available packages.
`module avail -a -i "pattern"` or `module avail \| grep -i "pattern"`	Search the available package list that matches “pattern”. Note the second option is given as some versions of `module` do not support case-insensitive search (`-i` option).
`module load <program>`	Load the program and make it available for use.
`module unload <program>`	Unload the program (removes it from your PATH).

For example, on our training HPC, you can try to run module avail to see which software is available. We can see an example using the software called bowtie2. If we try to use this software at the moment we get an error:

bowtie2 --version

Command 'bowtie2' not found, but can be installed with:

apt install bowtie2
Please ask your administrator.

But if we load the software first, then the command works:

module load bowtie/2.5.0
bowtie2 --version

/usr/local/Cluster-Apps/bowtie/2.5.0/bowtie2-align-s version 2.5.0
64-bit
Built on login-e-12
Mon 14 Nov 12:11:12 UTC 2022
Compiler: gcc version 4.8.5 20150623 (Red Hat 4.8.5-44) (GCC)
Options: -O3 -msse2 -funroll-loops -g3 -std=c++11 -DPOPCNT_CAPABILITY -DNO_SPINLOCK -DWITH_QUEUELOCK=1
Sizeof {int, long, long long, void*, size_t, off_t}: {4, 8, 8, 8, 8, 8}

If you echo $PATH, you will notice the installer has been added to your PATH variable (the environment variable that tells the shell where to find programs to run). Once you run module unload bowtie2/2.5.0, and then echo $PATH again, you notice the PATH variable will have been modified. This is how the Environment Modules package makes software available for you to use.

If a package is not available through the module command, your only option is to contact the HPC admin and ask them to install it for you. Alternatively, you can use a package manager as we briefly explain in the next section.

4.2 Package managers

Often you may want to use software packages that are not be installed by default on the HPC. There are several ways you could manage your own software installation, one of the most popular ones being the use of the package manager Conda or its newer implementation Mamba.

Covering Conda/Mamba is out of the scope of these materials, but you may want to attend our accompanying course Managing bioinformatics software and pipelines to learn more about this topic.

Very briefly, you manage packages by encapsulating them in an environment, and then activating the environment to make the software available. For example, we can install the toy program called cowpy:

# Create an environment
mamba create -n cowpy

# Install the software
mamba install -n cowpy conda-forge::cowpy=1.1.5

To use the packages installed in an environment, it needs to be activated:

mamba activate cowpy

An important note when working on a HPC is that you need to add some Mamba configuration commands before activating the environment. This would be our SLURM script:

#!/bin/bash
#SBATCH ... SLURM options here ...

# Always add these two commands when using mamba
eval "$(conda shell.bash hook)"
source $CONDA_PREFIX/etc/profile.d/mamba.sh

# then you can activate the environment
mamba activate cowpy

# the rest of your code...

This is because when we submit jobs to SLURM the jobs will start in a non-interactive shell, and mamba doesn’t get automatically set. Running the commands shown will ensure mamba activate works as expected.

Mamba versus Module

Although Mamba is a great tool to manage your own software installation, the disadvantage is that the software is not compiled specifically taking into account the hardware of the HPC. This is a slightly technical topic, but the main practical consequence is that software installed by HPC admins and made available through the module system may sometimes run faster than software installed via conda. This means you will use fewer resources and your jobs will complete faster.

Installing Mamba

Before you use Mamba, you will need to install it on the HPC. If you are attending our live course, we already have Mamba installed, so you can skip this step.

The default instructions below install Mamba on the user’s home. However, for Cambridge HPC users, we recommend installing Mamba on the fast storage partition called RDS.

Users from other institutions may want to adjust the dir_to_install variable given in the code block below, and install miniforge in a directory of their choice.

dir_to_install="$HOME/miniforge3"
wget "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
bash Miniforge3-$(uname)-$(uname -m).sh -b -p $dir_to_install
rm Miniforge3-$(uname)-$(uname -m).sh
$dir_to_install/bin/mamba shell init --shell bash

dir_to_install="$HOME/rds/hpc-work/miniforge3"
wget "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
bash Miniforge3-$(uname)-$(uname -m).sh -b -p $dir_to_install
rm Miniforge3-$(uname)-$(uname -m).sh
$dir_to_install/bin/mamba shell init --shell bash

Logout of the HPC and log back in again, to restart your terminal.

Then run the following commands:

conda config --add channels nodefaults
conda config --add channels bioconda
conda config --add channels conda-forge
conda config --set channel_priority strict
conda config --set remote_read_timeout_secs 1000

The software installation “recipes” used by Mamba are maintained by large communities of software developers. These communities are organised by channels, i.e. software repositories. Two popular channels are:

conda-forge - maintains several data science packages
bioconda - maintains bioinformatics software

The setup instructions given above add these channels to our Mamba setup, so that it looks for software in those repositories by default.

4.3 Containers

Another option to run specialised software on a HPC is to run your commands within pre-built containers. Containers are a technology that can be used to create and manage computational environments. A container is a lightweight, standalone executable package that contains everything needed to run a piece of software, including the operating system, libraries, and application code. Containers are isolated from the host system, meaning that they can run the same software in different environments without conflicts or interference.

We will focus on one of the most popular container platforms for cluster systems: Singularity (also known as Apptainer). Singularity is a free and open-source computer program that performs operating-system-level virtualization also known as containerization. Singularity is also designed to create and manage isolated environments as Docker, which is another popular and widely used container platform (i.e. images created with docker can be compatible with Singularity and vice versa).

Docker vs singularity

There are some key differences between Docker containers and Singularity containers. The most important being the necessary permission level of the containers. Docker containers run as root by default, which means that they have full access to the host system. While this can be advantageous in some cases, it can also pose security risks, particularly in multi-user environments. Singularity, on the other hand, runs containers as non-root users by default, which can improve security and prevent unauthorized access to the host system. Singularity is specifically designed for use in HPC environments and can run on a wide variety of platforms and systems without root access.

In summary:

Docker is well-suited for building and distributing software across different platforms and operating systems
Singularity is specifically designed for use in HPC environments and can provide improved security and performance in those settings.

4.3.1 Singularity installation

Typically, Singularity is pre-installed on HPC servers by the system administrators, and we recommend that you use the version installed by your system admins.

Although it is possible to install it yourself (e.g. with Mamba), we have found this to be a less reliable way to setup Singularity on a HPC. This is because it requires further configuration to interact with the filesystem (in particular as we submit jobs to SLURM).

4.3.2 Singularity images

Although you can build your own Singularity images, for many popular software there are already pre-built images available from public repositories. A very popular one being depot.galaxyproject.org maintained by the Galaxy project.

For example, let’s consider the SeqKit program, which is a toolkit for manipulating FASTA/Q files.

First, go to depot.galaxyproject.org and search for the software of interest (use Ctrl + F to find the text of interest).
When you find the software and version of interest, right-click the file and click “Copy Link”.

Then use that link with the singularity pull command:

# create a directory for our singularity images
mkdir images

# download the image
singularity pull images/seqkit-2.8.0.sif https://depot.galaxyproject.org/singularity/seqkit%3A2.8.0--h9ee0642_0

Here, we are saving the image file as seqkit-2.8.0.sif (.sif is the standard extension for singularity images). Once we have this image available, we are ready to run the software, which will see in practice with the exercise below.

4.4 Exercises

Exercise 1 - Using pre-installed software with module

This exercise is composed of two equivalent sub-exercises:

A bioinformatics-flavoured example of indexing a reference genome with the Bowtie 2 software.
A more generic example that uses a Python script to run a simulation.

You can choose one of the two to start with (whichever one suits your work better), and then do the other one if you also have time.

Make sure you are in the workshop folder (cd ~/rds/hpc-work/hpc_workshop).

In the data/reads folder, you will find several files related to whole-genome sequencing of individuals from the model organism Drosophila melanogaster (fruit fly). Our goal is to align our sequences to the reference genome, using a software called Bowtie 2.

But first, we need to prepare our genome for this alignment procedure, known as genome indexing. We have a file with the Drosophila genome in data/genome/drosophila_genome.fa.

Use module to search for Bowtie 2.5.0
Open the script in job_scripts/drosophila_genome_indexing.sh and:
- Fix your username in the #SBATCH -D option.
- Load the Bowtie2 module.
Submit the script to SLURM using sbatch, check it’s progress, and whether it ran successfully. Upon successful completion, you should get a directory results/drosophila/genome/ with several files inside it.

Answer

Step 1

We searched for the package using:

module avail --contains "bowtie"

There were several entries on our specific system (these will look different on a different HPC):

------------------------------------------------------------------------------------ /usr/local/software/spack/current/share/spack/tcl/linux-rhel7-x86_64 ------------------------------------------------------------------------------------
bowtie2-2.3.1-gcc-5.4.0-zbgr25s  bowtie2-2.3.1-intel-17.0.4-rir5xcg  bowtie2-2.3.4.1-gcc-5.4.0-td2qanw  bowtie2-2.3.5-intel-17.0.4-ypnhvtd

--------------------------------------------------------------------------------------------------- /usr/local/Cluster-Config/modulefiles ----------------------------------------------------------------------------------------------------
bowtie/1.0.1  bowtie/2.2.3  bowtie/2.5.0

We noticed that Bowtie version 2.5.0 is named bowtie/2.5.0.

Step 2

We opened our script in nano:

nano job_scripts/drosophila_genome_indexing.sh

We fixed it to include our username in the #SBATCH -D option, and we replaced the word “FIXME” further down to load the module found in the previous step:

# load bowtie2 module
module load bowtie/2.5.0

We saved the file and exited nano: Ctrl + X → Y → Enter.

Answer 3

After saving the script, we submitted it to the scheduler:

sbatch job_scripts/drosophila_genome_indexing.sh

We monitored the progress using the usual commands available to us:

squeue --me to check the queue
seff JOBID to check the efficiency of the job

The job completed successfully with several files inside the results directory:

ls results/drosophila/genome/

index.1.bt2  index.2.bt2  index.3.bt2  index.4.bt2  index.rev.1.bt2  index.rev.2.bt2

Make sure you are in the workshop folder (cd ~/rds/hpc-work/hpc_workshop).

A PhD student is working on project to understand how different patterns, such as animal stripes and coral colonies, form in nature. They are using a type of model, first proposed by Alan Turing, which models the interaction between two components that can difuse in space and promote/inhibit each other.

Click for more about this model

Turing patterns can be generated with a type of mathematical model called a “Reaction-diffusion system”. It models two substances - A and B - that can difuse in space and interact with each other in the following way: substance A self-activates and also activates B, while B inhibits A.

This seemingly simple interaction can generate complex spatial patterns, some of which capture the diversity of patterns observed in nature. Here is a very friendly video illustrating this: https://youtu.be/alH3yc6tX98.

The student has a python script that runs this model taking two input parameters and outputs an image file with the final result of the model. The two main parameters in the model are called “feed” and “kill”, and their python script accepts these as options, for example:

python3 analysis_scripts/turing_pattern.py --feed 0.04 --kill 0.06 --outdir results/turing/

This would produce an image saved as results/turing/f0.04_k0.06.png.

The python script has two package dependencies: NumPy and Matplotlib:

Use module to search for NumPy 1.22.4 and Matplotlib 3.5.2
Open the script in job_scripts/turing_pattern.sh and:
- Fix your username in the #SBATCH -D option.
- Load the NumPy and Matplotlib modules you found in the previous step.
Submit the script to SLURM using sbatch, check it’s progress, and whether it ran successfully. Upon successful completion, you should get a directory results/turing with an image file inside it.

Bonus step - view your image on the terminal

To see the full resolution image file, you will have to copy it back to your computer (we will cover this later). However, for a quick inspection you can use the terminal-based image viewer viu. This is not installed on the HPC, but you can install it using Mamba:

# Create an environment and install viu
mamba create -n viu conda-forge::viu

# Activate the environment
mamba activate viu

# View your image
viu PATH-TO-PNG-FILE

Answer

Step 1

We searched for each package using:

module avail --contains "numpy"
module avail --contains "matplotlib"

There were several entries on our specific system (these will look different on a different HPC):

------------------------------------------------------------------------------- /usr/local/software/spack/spack-modules/icelake-20220710/linux-rocky8-icelake --------------------------------------------------------------------------------
py-numpy/1.22.4/gcc/intel-oneapi-mkl/3ewpoxmq

------------------------------------------------------------------------------- /usr/local/software/spack/spack-modules/icelake-20211027/linux-centos8-icelake -------------------------------------------------------------------------------
py-numpy/1.21.2/gcc/intel-oneapi-mkl/obuwtoaw

------------------------------------------------------------------------------------ /usr/local/software/spack/current/share/spack/tcl/linux-rhel7-x86_64 ------------------------------------------------------------------------------------
py-numpy-1.12.1-gcc-4.8.5-gcrqnde  py-numpy-1.12.1-gcc-5.4.0-cjrgw2k  py-numpy-1.12.1-gcc-5.4.0-vumdouh     py-numpy-1.13.1-gcc-5.4.0-ic34jq4     py-numpy-1.13.1-intel-17.0.4-urzke56
py-numpy-1.12.1-gcc-5.4.0-5pz3ai7  py-numpy-1.12.1-gcc-5.4.0-csccubw  py-numpy-1.12.1-intel-17.0.4-wrnarf7  py-numpy-1.13.1-gcc-5.4.0-mdenejw     py-numpy-1.13.1-intel-17.0.4-vp6sdks
py-numpy-1.12.1-gcc-5.4.0-6hd5mcv  py-numpy-1.12.1-gcc-5.4.0-kqhi6in  py-numpy-1.13.1-gcc-4.8.5-migcc3t     py-numpy-1.13.1-gcc-5.4.0-z5tc7pf     py-numpy-1.15.2-gcc-5.4.0-uynjpqm
py-numpy-1.12.1-gcc-5.4.0-757r5y7  py-numpy-1.12.1-gcc-5.4.0-m6uoyga  py-numpy-1.13.1-gcc-5.4.0-44akyte     py-numpy-1.13.1-intel-17.0.4-6irddln  py-numpy-1.15.2-gcc-5.4.0-zyob6x5
py-numpy-1.12.1-gcc-5.4.0-a5hrlcf  py-numpy-1.12.1-gcc-5.4.0-q6hmnbj  py-numpy-1.13.1-gcc-5.4.0-hncdogk     py-numpy-1.13.1-intel-17.0.4-chl3zcw  py-numpy-1.16.2-gcc-5.4.0-cjwdstl

We noticed that:

NumPy version 1.22.4 is named py-numpy/1.22.4/gcc/intel-oneapi-mkl/3ewpoxmq
Matplotlib version 3.5.2 is named py-matplotlib/3.5.2/gcc/intel-oneapi-mkl/q4rbeory

Step 2

We opened our script in nano:

nano job_scripts/turing_pattern.sh

We fixed it to include our username in the #SBATCH -D option, and we replaced the word “FIXME” further down to load the two modules found in the previous step:

# load numpy and matplotlib modules
module load py-numpy/1.22.4/gcc/intel-oneapi-mkl/3ewpoxmq
module load py-matplotlib/3.5.2/gcc/intel-oneapi-mkl/q4rbeory

We saved the file and exited nano: Ctrl + X → Y → Enter.

Answer 3

After saving the script, we submitted it to the scheduler:

sbatch job_scripts/turing_pattern.sh

We monitored the progress using the usual commands available to us:

squeue --me to check the queue
seff JOBID to check the efficiency of the job

The job completed successfully with a PNG file inside the results directory:

ls results/turing

f0.04_k0.06.png

Exercise 2 - Using singularity images

Make sure you are in the workshop folder (cd ~/rds/hpc-work/hpc_workshop).

To illustrate the use of Singularity, we will use the seqkit software to extract some basic statistics from the sequencing files in the data/drosophila directory. If you haven’t done so already, first download the container image with the commands shown above.

The way to run a command within a singularity container is:

singularity run PATH-TO-IMAGE YOUR COMMANDS HERE

Test your singularity image by running the command seqkit --help within the container.
Modify the script job_scripts/seqkit_singularity.sh and add the command seqkit stats data/reads/*.fastq.gz, running inside the image container.
Submit this script as a job.
- Where do you think the output will be saved?
- As an optional bonus, modify the command to output the results to a file called results/fastq_stats.txt.

Answer

The Singularity command to test our software is:

singularity run images/seqkit-2.8.0.sif seqkit --help

This prints the help documentation of the SeqKit software, confirming that our image is working and contains the intended software.

We are ready to run our analysis, and so we modify the SLURM submission script to include our intended command inside it:

#!/bin/bash
#SBATCH -p training  # name of the partition to run job on
#SBATCH -D /home/YOUR-USERNAME/rds/hpc-work/hpc_workshop/  # working directory
#SBATCH -o job_logs/seqkit.log  # standard output file
#SBATCH -c 1        # number of CPUs. Default: 1
#SBATCH --mem=1G    # RAM memory. Default: 1G
#SBATCH -t 00:10:00 # time for the job HH:MM:SS. Default: 1 min

singularity run images/seqkit-2.8.0.sif seqkit stats data/reads/*.fastq.gz

(Don’t forget to adjust the username in #SBATCH -D.)

In this case the output would be saved to the .log file. We could, instead, modify our command to save the output to a file using the standard > redirection operator:

singularity run images/seqkit-2.8.0.sif seqkit stats data/reads/*.fastq.gz > results/fastq_stats.txt

The output generated contains some basic statistics for our sequencing files:

file                             format  type  num_seqs  sum_len  min_len  avg_len  max_len
data/reads/SRR307023_1.fastq.gz  FASTQ   DNA      5,000  505,000      101      101      101
data/reads/SRR307023_2.fastq.gz  FASTQ   DNA      5,000  505,000      101      101      101
data/reads/SRR307024_1.fastq.gz  FASTQ   DNA      5,000  505,000      101      101      101

... etc ...

4.5 Summary

Key Points

The module tool can be used to search for and load pre-installed software packages on a HPC.
- This tool may not always be available on your HPC.
To install your own software, you can use the Mamba package manager.
- Mamba allows you to have separate “software environments”, where multiple package versions can co-exist on your system.
- Use mamba create -n ENV to create a new software environment and mamba install -n ENV PROGRAM to install a program on that environment.
- Use mamba activate ENV to “activate” the software environment and make all the programs installed there available.
- When submitting jobs to sbatch, remember to include the Mamba configuration commands at the start of your script:
```
```bash
  eval "$(conda shell.bash hook)"
  source $CONDA_PREFIX/etc/profile.d/mamba.sh
```
```
Software containers can be a reliable alternative to Mamba environments, with many pre-existing containers available at depot.galaxyproject.org.
- To download a software container from public repositories, use the singularity pull command.
- To run a command within the software container, use the singularity run command.